System, Method and Computer Readable Medium for Dynamical Tracking of the Risk for Hypoglycemia in Type 1 and Type 2 Diabetes

ABSTRACT

A system, method and non-transient computer readable medium for tracking hypoglycemia risk in patients with diabetes exercise. A system may include a digital processor configured to execute instructions to receive an input from each available data source of a plurality of intermittently available data sources; determine a plurality of probability signals for impending hypoglycemia, wherein each probability signal is based on one or more of the inputs from the available data sources or a lack of input from an unavailable data source; wherein a probability signal for each unavailable data source is assigned a value corresponding to a zone of uncertainty; and determine an aggregate risk of hypoglycemia based on the plurality of intermittently data sources by aggregating the plurality of probability signals.

CROSS REFERENCE TO RELATED APPLICATION

This application claims priority to U.S. Provisional Patent ApplicationNo. 62/244,496, filed on Oct. 21, 2015, the disclosure of which isincorporated herein by reference in its entirety.

BACKGROUND AND SUMMARY

People with diabetes face a life-long optimization problem: to maintainstrict glycemic control, as reflected by hemoglobin A1c (HbA1c), withoutincreasing their risk for hypoglycemia. While target HbA1c values of 7%or less result in decreased risk of micro- and macrovascularcomplications, the risk for severe hypoglycemia increases withtightening glycemic control. Consequently, hypoglycemia has beenimplicated as the primary barrier to optimal glycemic control. Thus, astrategy for achieving optimal diabetes control can only be successfulif both tracking of HbA1c and tracking of the risk for hypoglycemia areavailable.

HbA1c: In the early 1990's the landmark Diabetes Control andComplications Trial [DCCT, 1, 2, 3] and the Stockholm DiabetesIntervention study [4] clearly indicated that intensive insulintreatment can reduce the long-term complications of type 1 diabetes. In1998 the UK Prospective Diabetes Study Group established that intensivetreatment with insulin or with oral medications to maintain nearlynormal levels of glycemia markedly reduces chronic complications in type2 diabetes as well [5]. HbA1c was identified as the primary marker oflong-term average glucose control [6, 7] and still remains thegold-standard assay reflecting average glycemia widely accepted inresearch as a primary outcome for virtually all studies of diabetestreatment, and in the clinical practice as primary feedback to thepatient and the physician and a base for treatment optimization.

Hypoglycemia is common in T1DM [8] and becomes more prevalent in T2DMwith treatment intensification [9]. However, the DCCT also showed thatintensive treatment of diabetes can also increase the risk for severehypoglycemia (low blood glucose that could result in stupor,unconsciousness, and even death) [8]. Indeed, HbA1c has repeatedly beenproven to be an ineffective assessment of patients' risk forhypoglycemia. The DCCT concluded that only about 8% of severehypoglycemic episodes could be predicted from known variables, includingHbA1c [8]; later this prediction was improved to 18% by a structuralequation model using history of severe hypoglycemia, awareness, andautonomic symptom score [10]. In subsequent studies, HbA1c has neverbeen significantly associated with severe hypoglycemia [11, 12, 13, 14].Nevertheless, the physiological mechanisms of hypoglycemia were wellestablished by a number of studies that have investigated therelationships between intensive therapy, hypoglycemia unawareness, andimpaired counterregulation [15, 16, 17, 18] and concluded that recurrenthypoglycemia spirals into a “vicious cycle” known ashypoglycemia-associated autonomic failure (HAAF, [19]) observedprimarily in type 1, but also in type 2 diabetes [20]. The acute riskfor hypoglycemia was attributed to impairments in the systemic reactionto falling BG levels: in health, falling BG concentration triggers asequence of responses, beginning with attenuation of endogenous insulinproduction, followed by increase in glucagon and epinephrine and, if BGconcentration falls further, resulting in autonomic symptoms and/orneuroglycopenia; in type 1 diabetes, and to some extent in type 2diabetes, these defense mechanisms are impaired [21, 22, 23]. As aresult, hypoglycemia was identified as the primary barrier to optimaldiabetes control [24, 25]. The clinical optimization problem of diabeteswas therefore clearly formulated: reduce average glycemia and exposureto high blood glucose levels (thereby HbA1c), while preventinghypoglycemia.

Self-Monitoring of Blood Glucose (SMBG): Home BG meters offer convenientmeans for frequent BG determinations through. Most devices are capableof storing BG readings (typically over 150 readings) and have interfacesto download these readings into a computer. The meters are usuallyaccompanied by software that has capabilities for basic data analyses(e.g. calculation of mean BG, estimates of the average BG over theprevious two weeks, percentages in target, hypoglycemic andhyperglycemic zones, etc.), log of the data, and graphicalrepresentation (e.g. histograms, pie charts) [26, 27, 28, 29].Analytical methods based on SMBG data are discussed in the next section.

Tracking estimated HbA1c (eA1c): The present inventors published a newapproach to real-time dynamical estimation of HbA1c from infrequentself-monitoring (SMBG) data [30]. This method was designed to trackchanges in average glycemia and was based on a conceptually new approachto the retrieval of SMBG using a mathematical model to estimate HbA1c asthe measurable aggregated effect of the action of an underlyingdynamical system which translates ambient BG levels into HbA1c valuesthrough hemoglobin glycation [30]. A key feature of this approach, amongothers, is that it is capable of working with infrequent SMBG datatypical for type 2 diabetes, e.g. fasting readings on most days andoccasional (monthly) 7-point SMBG profiles. Thus, the eA1c algorithmdiffered from all previously introduced techniques by its use of anunderlying model that “filled in” the gaps between sparse SMBG values,thereby allowing continuous tracing of average glycemia. The presentinventors adopted this model-based approach because, while it isgenerally true that HbA1c is roughly proportional to the average BG of aperson over the past 2-3 months and a number of linear and nonlinearformulas have been used to describe this relationship [31-40], it isalso established that average BG estimated from HbA1c using a linearformula and average BG estimated from SMBG are discordant measures ofglycemic control [41]. The discrepancies have been quantified by thehemoglobin glycation index (HGI, equal to observed HbA1c—predictedHbA1c), where the prediction is a linear regression formula based onaverage BG derived from 7-point daily profiles collected quarterly [42],or on average fasting BG [43].

Risk Analysis of BG Data [44]: The computation of mean glucose valuesfrom SMBG data is typically used as a descriptor of overall glycemiccontrol. Computing pre- and post-meal averages and their difference canserve as an indication of the effectiveness of pre-meal bolus timing andamount. Similarly, the percentages of SMBG readings within, below, orabove preset target limits would serve as indication of the generalbehavior of BG fluctuations. The suggested limits are 70 and 180 mg/dl(3.9-10 mmol/l), which create three suggested by the DCCT and commonlyaccepted bands: hypoglycemia (BG<=70 mg/dl); normoglycemia (70 mg/dl<BG<=180 mg/dl) and hyperglycemia (BG>180 mg/dl) [1]. In a series ofstudies the present inventors have shown that specific risk analysis ofSMBG data could also capture long-term trends towards increased risk forhypoglycemia [11, 12, 13], and could identify 24-hour periods ofincreased risk for hypoglycemia [14, 45]. The sequential steps of theRisk Analysis are:

Symmetrization of the BG scale: A nonlinear transformation is applied tothe BG measurements scale to map the entire BG range (20 to 600 mg/dl,or 1.1 to 33.3 mmol/l) to a symmetric interval. This is needed becausethe distribution of BG values of a person with diabetes is asymmetric,typically skewed towards hyperglycemia. The BG value of 112.5 mg/dl(6.25 mmol/l) is mapped to zero, corresponding to zero risk for hypo- orhyperglycemia (the present inventors should note that this is not anormoglycemic or fasting value, which in health would be <100 mg/dl; itis zero-risk value pertinent to diabetes). The analytical form of thistransformation is f(BG)=γ/.[In(BG)^(α)-β], where the parameters areestimated as α=1.084, β=5.381, and γ=1.509, if BG is measured in mg/dland α=1.026, β=1.861, and γ=1.794, if BG is in mmol/l [46].

Computing measures of risk for hypoglycemia and hyperglycemia: Aquadratic risk function is defined as by the formula r(BG)=10.f(BG)².The function r(BG) ranges from 0 to 100. Its minimum value is achievedat BG=112.5 mg/dl, a safe euglycemic BG reading, while its maximum isreached at the extreme ends of the BG scale. Thus, r(BG) can beinterpreted as a measure of the risk associated with a certain BG level.The left branch of this parabola identifies the risk of hypoglycemia,while the right branch identifies the risk of hyperglycemia. Now, letx₁, x₂, . . . x_(n) be a series of n BG readings, and let rl(BG)=r(BG)if f(BG)<0 and 0 otherwise; rh(BG)=r(BG) if f(BG)>0 and 0 otherwise.Then the Low and High Blood Glucose Indices are computed as follows:

${LBGI} = {\frac{1}{n}{\sum\limits_{i = 1}^{n}{{rl}\left( x_{i} \right)}^{2}}}$${HBGI} - {\frac{1}{n}{\sum\limits_{i = 1}^{n}{{rh}\left( x_{i} \right)}^{2}}}$

Thus, the LBGI is a non-negative quantity that increases when the numberand/or extent of low BG readings increases and the HBGI is non-negativequantity that increases when the number and/or extent of high BGreadings increases.

Chronic and Acute Risk for hypoglycemia: For the purposes of thisdisclosure the present inventors will refer to two types of risk factorsfor hypoglycemia—chronic, reflecting elevated long-term risk for, andacute, reflecting abrupt changes in metabolic status, which increase therisk for immediate hypoglycemia.

Chronic risk factors for hypoglycemia, including low HbA_(1c,) historyof severe hypoglycemia (SH), unawareness, and intensive therapy, werestudied by the DCCT [8] and others [20]. Perhaps the most importantmessages of these studies are: (i) a major [categorical rather thanordinal] predictor of future SH was the history of SH, and (ii) HbA_(1c)has a modest contribution to the prediction of SH (only 8% in theDCCT,8]. In contrast, variability-based measures accounted for 40-50% ofthe chronic risk for significant hypoglycemia [12]. As a result, the2005 American Diabetes Association consensus statement on hypoglycemiaconcluded that “. . . history of severe hypoglycemia and lower HbA_(1c)levels have limited ability to predict additional episodes . . .[while] >50% of hypoglycemia can be predicted based on risk analysis ofself-monitored plasma glucose data over time” [47]. Thus, it has beendemonstrated that patterns of chronically elevated (over weeks) risk forhypoglycemia is detectable from SMBG data [13].

Acute risk for hypoglycemia: In health, falling BG concentrationtriggers a sequence of responses, beginning with attenuation ofendogenous insulin production, and followed by increase in glucagon andepinephrine [21]. In T1DM endogenous insulin secretion is practicallynon-existent, thus the first defense mechanism against hypoglycemia isunavailable. Further, it has been shown that glucagon response isimpaired [48), and epinephrine response is typically attenuated [15].Antecedent hypoglycemia has been shown to shift to lower BG thethresholds for autonomic, symptomatic, and cognitive responses tosubsequent hypoglycemia, thereby impairing glycemic defenses andreducing detection of hypoglycemia [49]. These effects are summarized bythe concept of HAAF [19]. Thus, HAAF is a result of hormonal deficiencyand behavioral triggers, elevating risks for hypoglycemia on the timeframe of a few days. Specific 48-hour SMBG patterns of acutely increasedrisk for hypoglycemia have been associated with SH and recurrenthypoglycemic episodes [45].

Multi-Source Estimation of the Risk for Hypoglycemia: U.S. Pat. No.6,923,763 B1 issued on Aug. 2, 2005 [50] pointed to the possibility ofusing multiple data sources relevant to the various factors determiningthe chronic and acute risks for hypoglycemia to determine a compoundrisk for hypoglycemia. This technology was based on, among other things,a mathematical model using SMBG data as well as information about priorinsulin delivery, information about exercise based on heart rate signal,and information about autonomic system activation based on heart ratevariability. The system, method, and computer readable medium proposedhere is unique in its concept and mathematical methods due to, but notlimited thereto, the following: the present inventors now combinemulti-source data that using a stochastic probability aggregationprocedure which allows these various data sources to be available or notavailable to the overall risk estimation; in contrast, for example, thepresent inventors' previous technology relied on a single deterministicmodel requiring all inputs from all data sources to be availablesimultaneously.

Presently disclosed is a method for tracking hypoglycemia risk thatincludes obtaining an input from each available data source of aplurality of intermittently available data sources; determining aplurality of probability signals for impending hypoglycemia, whereineach probability signal is based on one or more of the inputs from theavailable data sources or a lack of input from an unavailable datasource; wherein a probability signal for each unavailable data source isassigned a value corresponding to a zone of uncertainty; and determiningan aggregate risk of hypoglycemia based on the plurality ofintermittently data sources by aggregating the plurality of probabilitysignals. In some embodiments, one data source of the plurality ofintermittently available data sources comprises self-monitoring bloodglucose (SMBG) data. In some embodiments, determining the plurality ofprobability signals for impending hypoglycemia includes determining achronic risk and/or an acute risk of hypoglycemia based on the SMBGdata. In some embodiments, obtaining the self-monitoring blood glucose(SMBG) data comprises receiving a blood glucose signal from a continuousblood glucose monitor. In some embodiments, the plurality ofintermittently available data sources includes one or more of: aphysical activity indication, an insulin delivery indication, acarbohydrate indication, and a non-insulin medicine indication. In someembodiments, the physical activity indication comprises a signal from atleast one sensor configured to detect when the user begins to exercise.In some embodiments, one or more of the plurality of intermittentlyavailable data sources are automatically monitored and reported. In someembodiments, one or more of the plurality of intermittently availabledata sources are self-reported by a user. In some embodiments,determining a plurality of probability signals for impendinghypoglycemia comprises translating each input from the available datasources into the probability signal for impending hypoglycemia, In someembodiments, the probability signal for impending hypoglycemia isstandardized on a scale where minimal risk of hypoglycemia is mapped tozero, maximal risk of hypoglycemia is mapped to 1, a cutoff valuedifferentiating no-risk and elevated risk is mapped to 0.5, and the zoneof certainty in determining risk of hypoglycemia is mapped to 0.5. Insome embodiments, the method further includes using the aggregate riskof hypoglycemia to estimate the probability of a hypoglycemic event. Insome embodiments, aggregating the plurality of probability signalsincludes combining the plurality of probability signals using the Bayesformula. In some embodiments, the method further includes displaying analert on a display of a portable computing device based on thedetermined aggregated risk of hypoglycemia. In some embodiments, themethod further includes communicating an instruction to an insulin pumpbased on the determined aggregated risk of hypoglycemia.

Also disclosed is a system for tracking hypoglycemia risk that includesa digital processor; and a memory in communication with the digitalprocess, wherein the memory contains instructions configured to beexecuted by the processor to receive an input from each available datasource of a plurality of intermittently available data sources;determine a plurality of probability signals for impending hypoglycemia,wherein each probability signal is based on one or more of the inputsfrom the available data sources or a lack of input from an unavailabledata source; wherein a probability signal for each unavailable datasource is assigned a value corresponding to a zone of uncertainty; anddetermine an aggregate risk of hypoglycemia based on the plurality ofintermittently data sources by aggregating the plurality of probabilitysignals. In some embodiments, the system further includes a display; andwherein the digital processor is configured to generate an alert on thedisplay if the determined aggregate risk of hypoglycemia indicates aprobability of a hypoglycemic event exceeds a predetermined threshold.In some embodiments, the system further includes a continuous bloodglucose monitoring sensor in communication with the digital processor,the continuous blood glucose monitoring sensor configured to generateself-monitored blood glucose data and communicate said data to thedigital processor. In some embodiments, the system further includes aninsulin pump in communication with the digital processor and configuredto dispense or not dispense insulin in response the determined aggregaterisk of hypoglycemia.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference is made to the accompanying drawings in which particularembodiments are illustrated as described in more detail in thedescription below.

FIGS. 1A and 1B are exemplary graphs of odds ratio and standardizationfunction for hypoglycemia based on chronic risk.

FIGS. 2A and 2B are exemplary graphs of odds ratio and standardizationfunction for hyperglycemia based on acute risk.

FIGS. 3A and 3B are exemplary graphs of odds ratio and standardizationfunction for hypoglycemia based on exercise level.

FIGS. 4A and 4B are graphs of Receiver Operating Characteristic curvesfor classifiers of upcoming hypoglycemia based on chronic risk alone.

FIG. 5 is a high level functional block diagram of an embodiment of thepresent invention.

FIG. 6A is a high level functional block diagram of a computing device.

FIG. 6B is an exemplary network system in which embodiments of thepresent invention can be implemented.

FIG. 7 is an exemplary block diagram of a system including a computersystem and an Internet connection.

FIG. 8 is an exemplary system in which one or more embodiments of theinvention can be implemented using a network.

FIG. 9 is a block diagram of an exemplary machine upon which one or moreaspects of the invention can be implemented.

FIG. 10 an exemplary graph of odds ratios based on multiple dependentsources.

DETAILED DESCRIPTION OF THE DRAWINGS

An aspect of an embodiment of the present invention provides, but is notlimited thereto, a system, method, and computer readable medium fordynamical tracking of the risk for hypoglycemia in type 1 and type 2diabetes using multiple information sources.

Data Sources: The method (and system and computer readable medium) fordynamical tracking of the risk for hypoglycemia estimates theprobability of an upcoming hypoglycemic event (BG below 70 mg/dL) usingdata from multiple potentially available sources that may include:

-   -   (1) Self-monitoring (SMBG) data transformed into BG risk values        as described above are used to determine:        -   a. Chronic risk for hypoglycemia (developing over weeks),            which in type 2 diabetes is typically the risk for            hypoglycemia associated with fasting (morning) BG over this            period of time, and in type 1 diabetes can be at any time            during the day;        -   b. Acute risk for hypoglycemia (developing over a few days),            which in type 2 diabetes is typically the risk for            hypoglycemia associated with fasting (morning) BG over this            period of time, and in type 1 diabetes can be at any time            during the day;    -   (2) Information about recent physical activity, which can be:        -   a. A self-report using a certain estimation scale, e.g.            less-usual-more physical activity or a 0-6 rating of            physical activity relative to the patient's baseline, or        -   b. Derived from other data sources such as monitoring of            heart rate, accelerometer, or other motion signals.    -   (3) Information about recent insulin delivery, which can be:        -   a. A self-report using a certain estimation scale, e.g.            less-usual-more insulin or a 0-6 rating of recent insulin            amount relative to the patient's baseline, or        -   b. Derived from automated data sources such as insulin pump            or insulin pen reports;    -   (4) Information about recent carbohydrate intake, which can be:        -   a. A self-report using a certain estimation scale, e.g.            less-usual-more food or a 0-6 rating of the amount of            recently ingested carbohydrates, or        -   b. Derived from automated data sources such as carb logs, or            image recognition of foods;    -   (5) Information about other medications that could trigger        hypoglycemia, such a sulfonylurea, GLP-1 agonists, or glucagon        suppressors, which can be:        -   a. Self-reported, or        -   b. Derived from automated data sources such as drug            injection pens.

The present inventors should emphasize that not all data sources need tobe available simultaneously—any combination of the above data sources,or any other available data sources can be used, as long as the data isprocessed using the following data standardization procedure:

Data Standardization Procedure: The present inventors translate theoutput of each data source into probability for impending hypoglycemia.The idea is that at each data source could potentially provide anassessment of the risk for hypoglycemia (e.g. more exercise, less food,or higher acute risk that results from transient autonomic failure),which can be converted into probability for impending hypoglycemia bydefining a function that maps the aggregated results from any datasource into probability space as follows:

-   -   (i) The least (minimal) risk indicated by the data source is        mapped to 0;    -   (ii) The most (maximal) risk indicated by the data source is        mapped to 1;    -   (iii) The cutoff value differentiating no-risk vs. elevated risk        for hypoglycemia suggested by the data source is mapped to 0.5;    -   (iv) If the data suggest a zone of uncertainty indicating        neither lower nor elevated risk for hypoglycemia, the entire        zone of uncertainty is mapped to probability to 0.5.

Data standardization is performed for all available data sources atevery step of the dynamical tracking procedure. Note that different datasources may be available at different times of the dynamical trackingprocess. If a data source is temporarily or permanently unavailable, theentire range of this data source is treated as uncertainly zone, meaningthat the procedure computing the probability for impending hypoglycemiastill passes through this predefined data source, but its output doesnot change as a result. This is done in order to accommodate temporarilymissing data or available data that fall into an uncertainty zone usingthe same computational sequence. This procedure is detailed below.

Available DATA: SMBG and Behavioral Records were available from NIHstudies of behavioral interventions targeting patient education abouthypoglycemia that were conducted between 1996 and 2009. All studyparticipants had the diagnosis of type1 diabetes. As reported in theliterature, the behavioral records were collected on hand-held computersasking several questions about recent patient behavior, including asubjective estimate of most recent Physical Activity [51, 52, 53, 54].

-   -   Study 1: 97 subjects; 12985 SMBG readings; 6705 behavioral        records    -   Study 2: 89 subjects; 15230 SMBG reading; 6209 behavioral        records    -   Study 3: 120 subjects; 188390 SMBG readings; 28224 behavioral        records

The data from Studies 1 and 2 were used as a training data set todevelop the models described below and to estimate all model parameters.Then, to validate the procedure, all models and all model parameterswere fixed and the resulting procedure was applied without any furtherchanges to the data from Study 3. This two-step approach ensures theapplicability of the procedure to data sets that are independent fromthe data used for its development.

Dynamical tracking of SMBG-based Risk: The following first-orderdynamical model is used to track the risk of hypoglycemia associatedwith SMBG readings, typically fasting BGs:

∂ t = - 1 τ Risk  ( - f  ( SMBG t ) )

where the driving function of the model f (SMBG_(t)) is obtained bycomputing the Low Blood Glucose Index (LBGI) on daily fasting BG (SMBGcollected between 6 AM and 10 AM) over several days. The duration ofthis time period depends on whether Chronic or Acute risk is assessedand can range from several weeks for Chronic Risk to a few days forAcute risk. The time constant of the model τ_(Risk) is a model-specificparameter which characterizes chronic vs. acute risk estimation asdescribed below:

Tracking the Chronic SMBG Risk for Hypoglycemia: For chronic riskcomponent estimation:

-   -   the driving function fChronic(SMBGt) equals the LBGI computed        from fasting (morning) SMBG values collected during the past        week;    -   the time constant of the dynamical model is fixed at        τChronicRisk=2 weeks.

The iterative procedure providing weekly estimates of the chronic riskruns as follows:

eChronicRisk(₀)=f _(Chronic)(SMBG_(t) ₀ )

eChronicRisk(t)=0.6065.eChronicRisk(t−1)+0.3935.f _(Chronic)(SMBG_(t))

The resulting weekly weights of the LBGI entering this calculation over5 weeks are then as follows:

The chronic risk may also be assessed more of less frequently thanweekly, by applying a simple discretization step to the dynamic equationabove. A daily chronic risk assessment, which would synchronize with thedaily acute risk assessment, would be computed as:

eChronicRisk(t ₀)=f _(Chronic)(SMBG_(t) ₀ )

eChronicRisk(t)=0.9311.eChronicRisk(t−1)+0.06894.f _(Chronic)(SMBG_(t))

Tracking the Acute SMBG Risk for Hypoglycemia: For the acute riskcomponent estimation:

the driving function fAcute(SMBGt) equals the LBGI computed from fasting(morning) SMBG values collected on the day of assessment;

the time constant of the dynamical model is fixed at τAcuteRisk=3 days.

The iterative procedure providing daily estimates of the chronic riskruns as follows:

eAcuteRisk(t ₀)=0.2835.f _(Acute)(SMBG_(t) ₀ )

eAcuteRisk(t)=0.7165.eAcuteRisk(t−1)+0.2835.f _(Acute)(SMBG_(t))

Acute risk estimates may not be available every day—missing SMBGreadings are handled by the data probability aggregation proceduredescribed below.

Standardization of the Chronic SMBG Risk for Hypoglycemia: The oddsratios for upcoming hypoglycemia based on the chronic risk alone wereused as a guideline to design the chronic risk data standardizationfunction as presented in FIGS. 1A and 2B. The points where the oddsratio changes its slope (FIG. 1A) are natural cut points for a piecewiselinear approximation of a standardization function (FIG. 1B).

Standardization of the Acute SMBG Risk for Hypoglycemia: The odds ratiosfor upcoming hypoglycemia based on the acute risk alone were used as aguideline to design the acute risk data standardization function aspresented in FIGS. 2A and 2B. The points where the odds ratio changesits slope (FIG. 2A) are natural cut points for a piecewise linearapproximation of a standardization function (FIG. 2B).

Standardization of Additional Signals: The mapping of data derived fromadditional signals, such as subjective ratings of recent physicalactivity, carbohydrate intake, or insulin delivery would depend on thespecifics of each signal and on its relationship to upcominghypoglycemia. However, the general mapping paradigm described above willhold: a change in the slope of the odds ratio for impending hypoglycemiawould indicate a cutoff point of the linear function mapping the dataonto probability for hypoglycemia. FIGS. 3A and 3B present an example ofsuch a mapping that uses the data from the training data set to producea probability for upcoming hypoglycemia based on a subjective rating ofrecent exercise rated on a scale from 0 (none) to 6 (more than usual). Asubjective exercise rating is converted into probability for impendinghypoglycemia using the change in the odds ratios observed in thetraining data (FIG. 3A). Because ratings below 3 indicate a relativelyflat ratio for impending hypoglycemia, these ratings are mapped to0.5—the uncertainty zone indicating neither lower nor higher risk forhypoglycemia.

Probability Aggregation from Multiple Data Sources: This is a stepwiseprocedure used to track dynamically the risk for hypoglycemia indicatedfrom multiple data sources. The data aggregation follows a classicalBayes formula, and the update is done in steps whenever new informationbecomes available from any of the data sources.

Step 1—the procedure is initialized with each individual's Chronic usingthe probability mapping in FIG. 1B as follows:

p ¹ _(hypo) =p ₁(eChronicRisk)

Step 2—if acute risk data is available, the probability for hypoglycemiais updated as follows:

$P_{hypo}^{2} = \frac{P_{hypo}^{1} \cdot {P_{2}({eAcuteRisk})}}{{P_{hypo}^{1} \cdot {P_{2}({eAcuteRisk})}} + {\left( {1 - P_{hypo}^{1}} \right) \cdot \left( {1 - {P_{2}({eAcuteRisk})}} \right)}}$

Step 3 and all subsequent steps: if additional signals are availableeach is entered though it's standardized probability mapping (e.g. FIG.3 as presented for exercise):

$P_{hypo}^{3} = \frac{P_{hypo}^{2} \cdot {P_{3}({Exercise})}}{{P_{hypo}^{2} \cdot {P_{3}({Exercise})}} + {\left( {1 - P_{hypo}^{2}} \right) \cdot \left( {1 - {P_{3}({Exercise})}} \right)}}$

It is important to note here that this derivation relies on theindependence of hypoglycemia from each data source, which allows toreplace P(A and B) by P(A)*P(B). In the case where this independence maynot be present (for example when food and insulin are added), theExercise update step can be replace by a “Non-SMBG” step, that combinesthe dependent data sources, where the odd ratios are computed for thecombination of the dependent sources (e.g. Exercise×Insulin) and P3 isderived in a multivariate manner as illustrated in FIG. 10.

Validation of the Tracking Procedure

As noted above, all model parameters and steps of the method weredeveloped and fixed using the training data sets of Studies 1 and 2 (seeAvailable data). After that the procedure was fixed and appliedprospectively to the independent test data set (Study 3). FIG. 4presents ROC (Receiver Operating Characteristic) curves for classifiersof upcoming hypoglycemia based on Chronic risk alone, Chronic+Acuterisk, and Chronic+Acute+Exercise risk, for the BG below 70 mg/dl (PanelA) and the BG below 50 mg/dl (Panel B). It is evident that theperformance of the procedure in the independent test data set isvirtually identical (even slightly better) than the performance in thetraining data used for its development, which indicates that theprocedure could be generalized to any other data set. FIGS. 4A and 4Billustrate ROC curves for a classifier of upcoming hypoglycemia: BG<70mg/dl (FIG. 4A) and BG<50 mg/dl (FIG. 4B). Predictably, the ROCcurvature (e.g. the performance of the procedure) increases with addingmore data sources.

Table 1 presents the results of FIG. 4 in numerical format, displayingthe odds ratios for impending hypoglycemia (defined as BG<70 mg/dl) atincreasingly higher thresholds for the aggregated probability forhypoglycemia based on Chronic, Acute, and Exercise-related risks, in thetraining and in the test data. It is evident that an aggregatedprobability of 0.7 or above indicates in a 2-fold or higher likelihoodfor future hypoglycemia; thus, the dynamical risk tracking procedureworks as intended.

TABLE 1 Odds Ratio for Hypoglycemia P threshold TRAINING TEST DATA 0.51.4403 1.5328 0.6 1.6661 1.7828 0.7 1.8631 1.9947 0.8 2.1014 2.1809 0.92.3702 2.2304

FIG. 5 is a high level functional block diagram of an embodiment of thepresent invention, or an aspect of an embodiment of the presentinvention.

As shown in FIG. 5, a processor or controller 102 communicates with theglucose monitor or device 101, and optionally the insulin device 100.The glucose monitor or device 101 communicates with the subject 103 tomonitor glucose levels of the subject 103. The processor or controller102 is configured to perform the required calculations. Optionally, theinsulin device 100 communicates with the subject 103 to deliver insulinto the subject 103. The processor or controller 102 is configured toperform the required calculations. The glucose monitor 101 and theinsulin device 100 may be implemented as a separate device or as asingle device. The processor 102 can be implemented locally in theglucose monitor 101, the insulin device 100, or a standalone device (orin any combination of two or more of the glucose monitor, insulindevice, or a stand along device). The processor 102 or a portion of thesystem can be located remotely such that the device is operated as atelemedicine device.

Referring to FIG. 6A, in its most basic configuration, computing device144 typically includes at least one processing unit 150 and memory 146.Depending on the exact configuration and type of computing device,memory 146 can be volatile (such as RAM), non-volatile (such as ROM,flash memory, etc.) or some combination of the two.

Additionally, device 144 may also have other features and/orfunctionality. For example, the device could also include additionalremovable and/or non-removable storage including, but not limited to,magnetic or optical disks or tape, as well as writable electricalstorage media. Such additional storage is the figure by removablestorage 152 and non-removable storage 148. Computer storage mediaincludes volatile and nonvolatile, removable and non-removable mediaimplemented in any method or technology for storage of information suchas computer readable instructions, data structures, program modules orother data. The memory, the removable storage and the non-removablestorage are all examples of computer storage media. Computer storagemedia includes, but is not limited to, RAM, ROM, EEPROM, flash memory orother memory technology CDROM, digital versatile disks (DVD) or otheroptical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other medium which canbe used to store the desired information and which can accessed by thedevice. Any such computer storage media may be part of, or used inconjunction with, the device.

The device may also contain one or more communications connections 154that allow the device to communicate with other devices (e.g. othercomputing devices). The communications connections carry information ina communication media. Communication media typically embodies computerreadable instructions, data structures, program modules or other data ina modulated data signal such as a carrier wave or other transportmechanism and includes any information delivery media. The term“modulated data signal” means a signal that has one or more of itscharacteristics set or changed in such a manner as to encode, execute,or process information in the signal. By way of example, and notlimitation, communication medium includes wired media such as a wirednetwork or direct-wired connection, and wireless media such as radio,RF, infrared and other wireless media. As discussed above, the termcomputer readable media as used herein includes both storage media andcommunication media.

In addition to a stand-alone computing machine, embodiments of theinvention can also be implemented on a network system comprising aplurality of computing devices that are in communication with anetworking means, such as a network with an infrastructure or an ad hocnetwork. The network connection can be wired connections or wirelessconnections. As a way of example, FIG. 6B illustrates a network systemin which embodiments of the invention can be implemented. In thisexample, the network system comprises computer 156 (e.g. a networkserver), network connection means 158 (e.g. wired and/or wirelessconnections), computer terminal 160, and PDA (e.g. a smart-phone) 162(or other handheld or portable device, such as a cell phone, laptopcomputer, tablet computer, GPS receiver, MP3 player, handheld videoplayer, pocket projector, etc. or handheld devices (or non-portabledevices) with combinations of such features). In an embodiment, itshould be appreciated that the module listed as 156 may be glucosemonitor device. In an embodiment, it should be appreciated that themodule listed as 156 may be a glucose monitor device and/or an insulindevice. Any of the components shown or discussed with FIG. 6B may bemultiple in number. The embodiments of the invention can be implementedin anyone of the devices of the system. For example, execution of theinstructions or other desired processing can be performed on the samecomputing device that is anyone of 156, 160, and 162. Alternatively, anembodiment of the invention can be performed on different computingdevices of the network system. For example, certain desired or requiredprocessing or execution can be performed on one of the computing devicesof the network (e.g. server 156 and/or glucose monitor device), whereasother processing and execution of the instruction can be performed atanother computing device (e.g. terminal 160) of the network system, orvice versa. In fact, certain processing or execution can be performed atone computing device (e.g. server 156 and/or glucose monitor device);and the other processing or execution of the instructions can beperformed at different computing devices that may or may not benetworked. For example, the certain processing can be performed atterminal 160, while the other processing or instructions are passed todevice 162 where the instructions are executed. This scenario may be ofparticular value especially when the PDA 162 device, for example,accesses to the network through computer terminal 160 (or an accesspoint in an ad hoc network). For another example, software to beprotected can be executed, encoded or processed with one or moreembodiments of the invention. The processed, encoded or executedsoftware can then be distributed to customers. The distribution can bein a form of storage media (e.g. disk) or electronic copy.

FIG. 7 is a block diagram that illustrates a system 130 including acomputer system 140 and the associated Internet 11 connection upon whichan embodiment may be implemented. Such configuration is typically usedfor computers (hosts) connected to the Internet 11 and executing aserver or a client (or a combination) software. A source computer suchas laptop, an ultimate destination computer and relay servers, forexample, as well as any computer or processor described herein, may usethe computer system configuration and the Internet connection shown inFIG. 7. The system 140 may be used as a portable electronic device suchas a notebook/laptop computer, a media player (e.g., MP3 based or videoplayer), a cellular phone, a Personal Digital Assistant (PDA), a glucosemonitor device, an insulin delivery device, an image processing device(e.g., a digital camera or video recorder), and/or any other handheldcomputing devices, or a combination of any of these devices. Note thatwhile FIG. 7 illustrates various components of a computer system, it isnot intended to represent any particular architecture or manner ofinterconnecting the components; as such details are not germane to thepresent invention. It will also be appreciated that network computers,handheld computers, cell phones and other data processing systems whichhave fewer components or perhaps more components may also be used. Thecomputer system of FIG. 7 may, for example, be an Apple Macintoshcomputer or Power Book, or an IBM compatible PC. Computer system 140includes a bus 137, an interconnect, or other communication mechanismfor communicating information, and a processor 138, commonly in the formof an integrated circuit, coupled with bus 137 for processinginformation and for executing the computer executable instructions.Computer system 140 also includes a main memory 134, such as a RandomAccess Memory (RAM) or other dynamic storage device, coupled to bus 137for storing information and instructions to be executed by processor138.

Main memory 134 also may be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by processor 138. Computer system 140 further includes a ReadOnly Memory (ROM) 136 (or other non-volatile memory) or other staticstorage device coupled to bus 137 for storing static information andinstructions for processor 138. A storage device 135, such as a magneticdisk or optical disk, a hard disk drive for reading from and writing toa hard disk, a magnetic disk drive for reading from and writing to amagnetic disk, and/or an optical disk drive (such as DVD) for readingfrom and writing to a removable optical disk, is coupled to bus 137 forstoring information and instructions. The hard disk drive, magnetic diskdrive, and optical disk drive may be connected to the system bus by ahard disk drive interface, a magnetic disk drive interface, and anoptical disk drive interface, respectively. The drives and theirassociated computer-readable media provide non-volatile storage ofcomputer readable instructions, data structures, program modules andother data for the general purpose computing devices. Typically computersystem 140 includes an Operating System (OS) stored in a non-volatilestorage for managing the computer resources and provides theapplications and programs with an access to the computer resources andinterfaces. An operating system commonly processes system data and userinput, and responds by allocating and managing tasks and internal systemresources, such as controlling and allocating memory, prioritizingsystem requests, controlling input and output devices, facilitatingnetworking and managing files. Non-limiting examples of operatingsystems are Microsoft Windows, Mac OS X, and Linux.

The term “processor” is meant to include any integrated circuit or otherelectronic device (or collection of devices) capable of performing anoperation on at least one instruction including, without limitation,Reduced Instruction Set Core (RISC) processors, CISC microprocessors,Microcontroller Units (MCUs), CISC-based Central Processing Units(CPUs), and Digital Signal Processors (DSPs). The hardware of suchdevices may be integrated onto a single substrate (e.g., silicon “die”),or distributed among two or more substrates. Furthermore, variousfunctional aspects of the processor may be implemented solely assoftware or firmware associated with the processor.

Computer system 140 may be coupled via bus 137 to a display 131, such asa Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), a flat screenmonitor, a touch screen monitor or similar means for displaying text andgraphical data to a user. The display may be connected via a videoadapter for supporting the display. The display allows a user to view,enter, and/or edit information that is relevant to the operation of thesystem. An input device 132, including alphanumeric and other keys, iscoupled to bus 137 for communicating information and command selectionsto processor 138. Another type of user input device is cursor control133, such as a mouse, a trackball, or cursor direction keys forcommunicating direction information and command selections to processor138 and for controlling cursor movement on display 131. This inputdevice typically has two degrees of freedom in two axes, a first axis(e.g., x) and a second axis (e.g., y), that allows the device to specifypositions in a plane.

The computer system 140 may be used for implementing the methods andtechniques described herein. According to one embodiment, those methodsand techniques are performed by computer system 140 in response toprocessor 138 executing one or more sequences of one or moreinstructions contained in main memory 134. Such instructions may be readinto main memory 134 from another computer-readable medium, such asstorage device 135. Execution of the sequences of instructions containedin main memory 134 causes processor 138 to perform the process stepsdescribed herein. In alternative embodiments, hard-wired circuitry maybe used in place of or in combination with software instructions toimplement the arrangement. Thus, embodiments of the invention are notlimited to any specific combination of hardware circuitry and software.

The term “computer-readable medium” (or “machine-readable medium”) asused herein is an extensible term that refers to any medium or anymemory, that participates in providing instructions to a processor,(such as processor 138) for execution, or any mechanism for storing ortransmitting information in a form readable by a machine (e.g., acomputer). Such a medium may store computer-executable instructions tobe executed by a processing element and/or control logic, and data whichis manipulated by a processing element and/or control logic, and maytake many forms, including but not limited to, non-volatile medium,volatile medium, and transmission medium. Transmission media includescoaxial cables, copper wire and fiber optics, including the wires thatcomprise bus 137. Transmission media can also take the form of acousticor light waves, such as those generated during radio-wave and infrareddata communications, or other form of propagated signals (e.g., carrierwaves, infrared signals, digital signals, etc.). Common forms ofcomputer-readable media include, for example, a floppy disk, a flexibledisk, hard disk, magnetic tape, or any other magnetic medium, a CD-ROM,any other optical medium, punch-cards, paper-tape, any other physicalmedium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM,any other memory chip or cartridge, a carrier wave as describedhereinafter, or any other medium from which a computer can read.

Various forms of computer-readable media may be involved in carrying oneor more sequences of one or more instructions to processor 138 forexecution. For example, the instructions may initially be carried on amagnetic disk of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 140 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infra-red signal and appropriatecircuitry can place the data on bus 137. Bus 137 carries the data tomain memory 134, from which processor 138 retrieves and executes theinstructions. The instructions received by main memory 134 mayoptionally be stored on storage device 135 either before or afterexecution by processor 138.

Computer system 140 also includes a communication interface 141 coupledto bus 137. Communication interface 141 provides a two-way datacommunication coupling to a network link 139 that is connected to alocal network 111. For example, communication interface 141 may be anIntegrated Services Digital Network (ISDN) card or a modem to provide adata communication connection to a corresponding type of telephone line.As another non-limiting example, communication interface 141 may be alocal area network (LAN) card to provide a data communication connectionto a compatible LAN. For example, Ethernet based connection based onIEEE802.3 standard may be used such as 10/100 BaseT, 1000 BaseT (gigabitEthernet), 10 gigabit Ethernet (10 GE or 10 GbE or 10 GigE per IEEE Std802.3ae-2002 as standard), 40 Gigabit Ethernet (40 GbE), or 100 GigabitEthernet (100 GbE as per Ethernet standard IEEE P802.3ba), as describedin Cisco Systems, Inc. Publication number 1-587005-001-3 (6/99),“Internetworking Technologies Handbook”, Chapter 7: “EthernetTechnologies”, pages 7-1 to 7-38, which is incorporated in its entiretyfor all purposes as if fully set forth herein. In such a case, thecommunication interface 141 typically include a LAN transceiver or amodem, such as Standard Microsystems Corporation (SMSC) LAN91C111 10/100Ethernet transceiver described in the Standard Microsystems Corporation(SMSC) data-sheet “LAN91C111 10/100 Non-PCI Ethernet Single ChipMAC+PHY” Data-Sheet, Rev. 15 (Feb. 20, 2004), which is incorporated inits entirety for all purposes as if fully set forth herein.

Wireless links may also be implemented. In any such implementation,communication interface 141 sends and receives electrical,electromagnetic or optical signals that carry digital data streamsrepresenting various types of information.

Network link 139 typically provides data communication through one ormore networks to other data devices. For example, network link 139 mayprovide a connection through local network 111 to a host computer or todata equipment operated by an Internet Service Provider (ISP) 142. ISP142 in turn provides data communication services through the world widepacket data communication network Internet 11. Local network 111 andInternet 11 both use electrical, electromagnetic or optical signals thatcarry digital data streams. The signals through the various networks andthe signals on the network link 139 and through the communicationinterface 141, which carry the digital data to and from computer system140, are exemplary forms of carrier waves transporting the information.

A received code may be executed by processor 138 as it is received,and/or stored in storage device 135, or other non-volatile storage forlater execution. In this manner, computer system 140 may obtainapplication code in the form of a carrier wave.

The concept of dynamical tracking of the risk for hypoglycemia in type 1and type 2 diabetes using multiple information have been developed anddisclosed herein; and may be implemented and utilized with the relatedprocessors, networks, computer systems, internet, and components andfunctions according to the schemes disclosed herein.

FIG. 8 illustrates a system in which one or more embodiments of theinvention can be implemented using a network, or portions of a networkor computers. Although the present invention glucose device may bepracticed without a network.

FIG. 8 diagrammatically illustrates an exemplary system in whichexamples of the invention can be implemented. In an embodiment theglucose monitor (and/or insulin pump) may be implemented by the subject(or patient) locally at home or other desired location. However, in analternative embodiment it may be implemented in a clinic setting orassistance setting. For instance, referring to FIG. 8, a clinic setup158 provides a place for doctors (e.g. 164) or clinician/assistant todiagnose patients (e.g. 159) with diseases related with glucose andrelated diseases and conditions. A glucose monitoring device 10 can beused to monitor and/or test the glucose levels of the patient—as astandalone device. It should be appreciated that while only glucosemonitor device 10 is shown in the figure, the system of the inventionand any component thereof may be used in the manner depicted by FIG. 8.The system or component may be affixed to the patient or incommunication with the patient as desired or required. For example thesystem or combination of components thereof—including a glucose monitordevice 10 (or other related devices or systems such as a controller,and/or an insulin pump, or any other desired or required devices orcomponents)—may be in contact, communication or affixed to the patientthrough tape or tubing (or other medical instruments or components) ormay be in communication through wired or wireless connections. Suchmonitor and/or test can be short term (e.g. clinical visit) or long term(e.g. clinical stay or family). The glucose monitoring device outputscan be used by the doctor (clinician or assistant) for appropriateactions, such as insulin injection or food feeding for the patient, orother appropriate actions or modeling. Alternatively, the glucosemonitoring device output can be delivered to computer terminal 168 forinstant or future analyses. The delivery can be through cable orwireless or any other suitable medium. The glucose monitoring deviceoutput from the patient can also be delivered to a portable device, suchas PDA 166. The glucose monitoring device outputs with improved accuracycan be delivered to a glucose monitoring center 172 for processingand/or analyzing. Such delivery can be accomplished in many ways, suchas network connection 170, which can be wired or wireless.

In addition to the glucose monitoring device outputs, errors, parametersfor accuracy improvements, and any accuracy related information can bedelivered, such as to computer 168, and/or glucose monitoring center 172for performing error analyses. This can provide a centralized accuracymonitoring, modeling and/or accuracy enhancement for glucose centers,due to the importance of the glucose sensors.

Examples of the invention can also be implemented in a standalonecomputing device associated with the target glucose monitoring device.An exemplary computing device (or portions thereof) in which examples ofthe invention can be implemented is schematically illustrated in FIG.6A.

FIG. 9 is a block diagram illustrating an example of a machine uponwhich one or more aspects of embodiments of the present invention can beimplemented.

FIG. 9 illustrates a block diagram of an example machine 400 upon whichone or more embodiments (e.g., discussed methodologies) can beimplemented (e.g., run).

Examples of machine 400 can include logic, one or more components,circuits (e.g., modules), or mechanisms. Circuits are tangible entitiesconfigured to perform certain operations. In an example, circuits can bearranged (e.g., internally or with respect to external entities such asother circuits) in a specified manner. In an example, one or morecomputer systems (e.g., a standalone, client or server computer system)or one or more hardware processors (processors) can be configured bysoftware (e.g., instructions, an application portion, or an application)as a circuit that operates to perform certain operations as describedherein. In an example, the software can reside (1) on a non-transitorymachine readable medium or (2) in a transmission signal. In an example,the software, when executed by the underlying hardware of the circuit,causes the circuit to perform the certain operations.

In an example, a circuit can be implemented mechanically orelectronically. For example, a circuit can comprise dedicated circuitryor logic that is specifically configured to perform one or moretechniques such as discussed above, such as including a special-purposeprocessor, a field programmable gate array (FPGA) or anapplication-specific integrated circuit (ASIC). In an example, a circuitcan comprise programmable logic (e.g., circuitry, as encompassed withina general-purpose processor or other programmable processor) that can betemporarily configured (e.g., by software) to perform the certainoperations. It will be appreciated that the decision to implement acircuit mechanically (e.g., in dedicated and permanently configuredcircuitry), or in temporarily configured circuitry (e.g., configured bysoftware) can be driven by cost and time considerations.

Accordingly, the term “circuit” is understood to encompass a tangibleentity, be that an entity that is physically constructed, permanentlyconfigured (e.g., hardwired), or temporarily (e.g., transitorily)configured (e.g., programmed) to operate in a specified manner or toperform specified operations. In an example, given a plurality oftemporarily configured circuits, each of the circuits need not beconfigured or instantiated at any one instance in time. For example,where the circuits comprise a general-purpose processor configured viasoftware, the general-purpose processor can be configured as respectivedifferent circuits at different times. Software can accordinglyconfigure a processor, for example, to constitute a particular circuitat one instance of time and to constitute a different circuit at adifferent instance of time.

In an example, circuits can provide information to, and receiveinformation from, other circuits. In this example, the circuits can beregarded as being communicatively coupled to one or more other circuits.Where multiple of such circuits exist contemporaneously, communicationscan be achieved through signal transmission (e.g., over appropriatecircuits and buses) that connect the circuits. In embodiments in whichmultiple circuits are configured or instantiated at different times,communications between such circuits can be achieved, for example,through the storage and retrieval of information in memory structures towhich the multiple circuits have access. For example, one circuit canperform an operation and store the output of that operation in a memorydevice to which it is communicatively coupled. A further circuit canthen, at a later time, access the memory device to retrieve and processthe stored output. In an example, circuits can be configured to initiateor receive communications with input or output devices and can operateon a resource (e.g., a collection of information).

The various operations of method examples described herein can beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors can constitute processor-implementedcircuits that operate to perform one or more operations or functions. Inan example, the circuits referred to herein can compriseprocessor-implemented circuits.

Similarly, the methods described herein can be at least partiallyprocessor-implemented. For example, at least some of the operations of amethod can be performed by one or processors or processor-implementedcircuits. The performance of certain of the operations can bedistributed among the one or more processors, not only residing within asingle machine, but deployed across a number of machines. In an example,the processor or processors can be located in a single location (e.g.,within a home environment, an office environment or as a server farm),while in other examples the processors can be distributed across anumber of locations.

The one or more processors can also operate to support performance ofthe relevant operations in a “cloud computing” environment or as a“software as a service” (SaaS). For example, at least some of theoperations can be performed by a group of computers (as examples ofmachines including processors), with these operations being accessiblevia a network (e.g., the Internet) and via one or more appropriateinterfaces (e.g., Application Program Interfaces (APIs).)

Example embodiments (e.g., apparatus, systems, or methods) can beimplemented in digital electronic circuitry, in computer hardware, infirmware, in software, or in any combination thereof. Exampleembodiments can be implemented using a computer program product (e.g., acomputer program, tangibly embodied in an information carrier or in amachine readable medium, for execution by, or to control the operationof, data processing apparatus such as a programmable processor, acomputer, or multiple computers).

A computer program can be written in any form of programming language,including compiled or interpreted languages, and it can be deployed inany form, including as a stand-alone program or as a software module,subroutine, or other unit suitable for use in a computing environment. Acomputer program can be deployed to be executed on one computer or onmultiple computers at one site or distributed across multiple sites andinterconnected by a communication network.

In an example, operations can be performed by one or more programmableprocessors executing a computer program to perform functions byoperating on input data and generating output. Examples of methodoperations can also be performed by, and example apparatus can beimplemented as, special purpose logic circuitry (e.g., a fieldprogrammable gate array (FPGA) or an application-specific integratedcircuit (ASIC)).

The computing system can include clients and servers. A client andserver are generally remote from each other and generally interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. Inembodiments deploying a programmable computing system, it will beappreciated that both hardware and software architectures requireconsideration. Specifically, it will be appreciated that the choice ofwhether to implement certain functionality in permanently configuredhardware (e.g., an ASIC), in temporarily configured hardware (e.g., acombination of software and a programmable processor), or a combinationof permanently and temporarily configured hardware can be a designchoice. Below are set out hardware (e.g., machine 400) and softwarearchitectures that can be deployed in example embodiments.

In an example, the machine 400 can operate as a standalone device or themachine 400 can be connected (e.g., networked) to other machines.

In a networked deployment, the machine 400 can operate in the capacityof either a server or a client machine in server-client networkenvironments. In an example, machine 400 can act as a peer machine inpeer-to-peer (or other distributed) network environments. The machine400 can be a personal computer (PC), a tablet PC, a set-top box (STB), aPersonal Digital Assistant (PDA), a mobile telephone, a web appliance, anetwork router, switch or bridge, or any machine capable of executinginstructions (sequential or otherwise) specifying actions to be taken(e.g., performed) by the machine 400. Further, while only a singlemachine 400 is illustrated, the term “machine” shall also be taken toinclude any collection of machines that individually or jointly executea set (or multiple sets) of instructions to perform any one or more ofthe methodologies discussed herein.

Example machine (e.g., computer system) 400 can include a processor 402(e.g., a central processing unit (CPU), a graphics processing unit (GPU)or both), a main memory 404 and a static memory 406, some or all ofwhich can communicate with each other via a bus 408. The machine 400 canfurther include a display unit 410, an alphanumeric input device 412(e.g., a keyboard), and a user interface (UI) navigation device 411(e.g., a mouse). In an example, the display unit 410, input device 412and UI navigation device 414 can be a touch screen display. The machine400 can additionally include a storage device (e.g., drive unit) 416, asignal generation device 418 (e.g., a speaker), a network interfacedevice 420, and one or more sensors 421, such as a global positioningsystem (GPS) sensor, compass, accelerometer, or other sensor.

The storage device 416 can include a machine readable medium 422 onwhich is stored one or more sets of data structures or instructions 424(e.g., software) embodying or utilized by any one or more of themethodologies or functions described herein. The instructions 424 canalso reside, completely or at least partially, within the main memory404, within static memory 406, or within the processor 402 duringexecution thereof by the machine 400. In an example, one or anycombination of the processor 402, the main memory 404, the static memory406, or the storage device 416 can constitute machine readable media.

While the machine readable medium 422 is illustrated as a single medium,the term “machine readable medium” can include a single medium ormultiple media (e.g., a centralized or distributed database, and/orassociated caches and servers) that configured to store the one or moreinstructions 424. The term “machine readable medium” can also be takento include any tangible medium that is capable of storing, encoding, orcarrying instructions for execution by the machine and that cause themachine to perform any one or more of the methodologies of the presentdisclosure or that is capable of storing, encoding or carrying datastructures utilized by or associated with such instructions. The term“machine readable medium” can accordingly be taken to include, but notbe limited to, solid-state memories, and optical and magnetic media.Specific examples of machine readable media can include non-volatilememory, including, by way of example, semiconductor memory devices(e.g., Electrically Programmable Read-Only Memory (EPROM), ElectricallyErasable Programmable Read-Only Memory (EEPROM)) and flash memorydevices; magnetic disks such as internal hard disks and removable disks;magneto-optical disks; and CD-ROM and DVD-ROM disks.

The instructions 424 can further be transmitted or received over acommunications network 426 using a transmission medium via the networkinterface device 420 utilizing any one of a number of transfer protocols(e.g., frame relay, IP, TCP, UDP, HTTP, etc.). Example communicationnetworks can include a local area network (LAN), a wide area network(WAN), a packet data network (e.g., the Internet), mobile telephonenetworks (e.g., cellular networks), Plain Old Telephone (POTS) networks,and wireless data networks (e.g., IEEE 802.11 standards family known asWi-Fi®, IEEE 802.16 standards family known as WiMax®), peer-to-peer(P2P) networks, among others. The term “transmission medium” shall betaken to include any intangible medium that is capable of storing,encoding or carrying instructions for execution by the machine, andincludes digital or analog communications signals or other intangiblemedium to facilitate communication of such software.

It should be appreciated that various sizes, dimensions, contours,rigidity, shapes, flexibility and materials of any of the components orportions of components in the various embodiments discussed throughoutmay be varied and utilized as desired or required. Similarly, locationsand alignments of the various components may vary as desired orrequired.

It should be appreciated that any of the components or modules referredto with regards to any of the present invention embodiments discussedherein, may be integrally or separately formed with one another.Further, redundant functions or structures of the components or modulesmay be implemented.

It should be appreciated that the device and related componentsdiscussed herein may take on all shapes along the entire continualgeometric spectrum of manipulation of x, y and z planes to provide andmeet the anatomical, environmental, and structural demands andoperational requirements. Moreover, locations and alignments of thevarious components may vary as desired or required.

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The following patents, applications and publications as listed below andthroughout this document are hereby incorporated by reference in theirentirety herein.

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In summary, while the present invention has been described with respectto specific embodiments, many modifications, variations, alterations,substitutions, and equivalents will be apparent to those skilled in theart. The present invention is not to be limited in scope by the specificembodiment described herein. Indeed, various modifications of thepresent invention, in addition to those described herein, will beapparent to those of skill in the art from the foregoing description andaccompanying drawings. Accordingly, the invention is to be considered aslimited only by the spirit and scope of the disclosure, including allmodifications and equivalents.

Still other embodiments will become readily apparent to those skilled inthis art from reading the above-recited detailed description anddrawings of certain exemplary embodiments. It should be understood thatnumerous variations, modifications, and additional embodiments arepossible, and accordingly, all such variations, modifications, andembodiments are to be regarded as being within the spirit and scope ofthis application. For example, regardless of the content of any portion(e.g., title, field, background, summary, abstract, drawing figure,etc.) of this application, unless clearly specified to the contrary,there is no requirement for the inclusion in any claim herein or of anyapplication claiming priority hereto of any particular described orillustrated activity or element, any particular sequence of suchactivities, or any particular interrelationship of such elements.Moreover, any activity can be repeated, any activity can be performed bymultiple entities, and/or any element can be duplicated. Further, anyactivity or element can be excluded, the sequence of activities canvary, and/or the interrelationship of elements can vary. Unless clearlyspecified to the contrary, there is no requirement for any particulardescribed or illustrated activity or element, any particular sequence orsuch activities, any particular size, speed, material, dimension orfrequency, or any particularly interrelationship of such elements.Accordingly, the descriptions and drawings are to be regarded asillustrative in nature, and not as restrictive. Moreover, when anynumber or range is described herein, unless clearly stated otherwise,that number or range is approximate. When any range is described herein,unless clearly stated otherwise, that range includes all values thereinand all sub ranges therein. Any information in any material (e.g., aUnited States/foreign patent, United States/foreign patent application,book, article, etc.) that has been incorporated by reference herein, isonly incorporated by reference to the extent that no conflict existsbetween such information and the other statements and drawings set forthherein. In the event of such conflict, including a conflict that wouldrender invalid any claim herein or seeking priority hereto, then anysuch conflicting information in such incorporated by reference materialis specifically not incorporated by reference herein.

What is claimed is:
 1. A method for tracking hypoglycemia riskcomprising: obtaining an input from each available data source of aplurality of intermittently available data sources; determining aplurality of probability signals for impending hypoglycemia, whereineach probability signal is based on one or more of the inputs from theavailable data sources or a lack of input from an unavailable datasource; wherein a probability signal for each unavailable data source isassigned a value corresponding to a zone of uncertainty; and determiningan aggregate risk of hypoglycemia based on the plurality ofintermittently data sources by aggregating the plurality of probabilitysignals.
 2. The method of claim 1, wherein one data source of theplurality of intermittently available data sources comprisesself-monitoring blood glucose (SMBG) data.
 3. The method of claim 2,wherein determining the plurality of probability signals for impendinghypoglycemia includes determining a chronic risk of hypoglycemia basedon the SMBG data by the formulaseChronicRisk(t ₀)=f _(Chronic)(SMBG_(t) ₀ )eChronicRisk(t)=β2.eChronicRisk(t−1)+β1.f _(Chronic)(SMBG_(t)) whereinβ1 and β2 are predefined constants.
 4. The method of claim 2, whereindetermining the plurality of probability signals for impendinghypoglycemia includes determining an acute risk of hypoglycemia based onthe SMBG data by the formulaseAcuteRisk(t ₀)=α1.f _(Acute)(SMBG_(t) ₀ )eAcuteRisk(t)=α2.eAcuteRisk(t−1)+α1.f _(Acute)(SMBG_(t)) wherein α1 andα2 are predefined constants.
 5. The method of claim 2, wherein obtainingthe self-monitoring blood glucose (SMBG) data comprises receiving ablood glucose signal from a continuous blood glucose monitor.
 6. Themethod of claim 1, wherein the plurality of intermittently availabledata sources includes one or more of: a physical activity indication, aninsulin delivery indication, a carbohydrate indication, and anon-insulin medicine indication.
 7. The method of claim 6, wherein thephysical activity indication comprises a signal from at least one sensorconfigured to detect when the user begins to exercise.
 8. The method ofclaim 1, wherein one or more of the plurality of intermittentlyavailable data sources are automatically monitored and reported.
 9. Themethod of claim 1, wherein one or more of the plurality ofintermittently available data sources are self-reported by a user. 10.The method of claim 1, wherein determining a plurality of probabilitysignals for impending hypoglycemia comprises translating each input fromthe available data sources into the probability signal for impendinghypoglycemia.
 11. The method of claim 1, wherein the probability signalfor impending hypoglycemia is standardized on a scale where minimal riskof hypoglycemia is mapped to zero, maximal risk of hypoglycemia ismapped to 1, a cutoff value differentiating no-risk and elevated risk ismapped to 0.5, and the zone of certainty in determining risk ofhypoglycemia is mapped to 0.5.
 12. The method of claim 1 furthercomprising: using the aggregate risk of hypoglycemia to estimate theprobability of a hypoglycemic event.
 13. The method of claim 1, whereinaggregating the plurality of probability signals includes combining theplurality of probability signals using the Bayes formula.
 14. The methodof claim 1, wherein aggregating the plurality of probability signalsincludes the steps of: determining an individual's chronic risk ofhypoglycemia based on self-monitored blood glucose data by the formulap ¹ _(hypo)=p ₁(eChronicRisk) if a probability signal is available foracute risk of hypoglycemia based on self-monitored blood glucose data isavailable, updating the aggregate risk of hypoglycemia by the formula$P_{hypo}^{2} = \frac{P_{hypo}^{1} \cdot {P_{2}({eAcuteRisk})}}{{P_{hypo}^{1} \cdot {P_{2}({eAcuteRisk})}} + {\left( {1 - P_{hypo}^{1}} \right) \cdot \left( {1 - {P_{2}({eAcuteRisk})}} \right)}}$and, for each additional probability signal, updating the aggregate riskof hypoglycemia by the formula${P_{hypo}^{3} = \frac{P_{hypo}^{2} \cdot {P_{3}({Exercise})}}{{P_{hypo}^{2} \cdot {P_{3}({Exercise})}} + {\left( {1 - P_{hypo}^{2}} \right) \cdot \left( {1 - {P_{3}({Exercise})}} \right)}}},$where “Exercise” indicates one of the data sources of the additionalprobability signal
 15. The method of claim 1 further comprising:displaying an alert on a display of a portable computing device based onthe determined aggregated risk of hypoglycemia.
 16. The method of claim1 further comprising: communicating an instruction to an insulin pumpbased on the determined aggregated risk of hypoglycemia.
 17. A systemfor tracking hypoglycemia risk comprising: a digital processor; a memoryin communication with the digital process, wherein the memory containsinstructions configured to be executed by the processor to receive aninput from each available data source of a plurality of intermittentlyavailable data sources; determine a plurality of probability signals forimpending hypoglycemia, wherein each probability signal is based on oneor more of the inputs from the available data sources or a lack of inputfrom an unavailable data source; wherein a probability signal for eachunavailable data source is assigned a value corresponding to a zone ofuncertainty; and determine an aggregate risk of hypoglycemia based onthe plurality of intermittently data sources by aggregating theplurality of probability signals.
 18. The system of claim 17 furthercomprising: a display; and wherein the digital processor is configuredto generate an alert on the display if the determined aggregate risk ofhypoglycemia indicates a probability of a hypoglycemic event exceeds apredetermined threshold.
 19. The system of claim 17 further comprising:a continuous blood glucose monitoring sensor in communication with thedigital processor, the continuous blood glucose monitoring sensorconfigured to generate self-monitored blood glucose data and communicatesaid data to the digital processor.
 20. The system of claim 17 furthercomprising: an insulin pump in communication with the digital processorand configured to dispense or not dispense insulin in response thedetermined aggregate risk of hypoglycemia.